Impact of decisions in the design process is initially high and declines as the design matures. However, few computational tools are available for the early design phase, thus an opportunity exists to create such tools. New technology opens up new possibilities to create new and novel computational tools. In this work an existing application is adapted for a new novel 3D input device that is named the Leap Motion controller. The controller allows the user to interact with 3D objects on the screen by using fingers and hands. The of result of this work is a conceptual design application which enables very direct manipulation of 3D objects on the screen, which has not before been achieved for this type of application in 3D. An improved human-computer interaction can potentially improve the users understanding of the structural behavior of a model, cognitive engagement in the design task, and encourage further design exploration. Three different cases are implemented which aims to enable the user to explore different design options with emphasis on geometrical form, as this has the greatest potential to improve the structural performance. The case studies demonstrate new potential for building engineering intuition and improving design space exploration through very direct manipulation in 3D.

Dual-stage positioning systems have been widely used in factory automation, robotic manipulators, high-density data storage systems, and manufacturing systems. Trajectory generation and control of dual-stage positioning systems is of great importance and is made complicated by the presence of physical and operational constraints. In this work, we describe how to generate feasible reference trajectories for a dual-stage positioning system consisting of a fine stage and a coarse stage, and how to use them in a model predictive control algorithm for which recursive feasibility is guaranteed. The reference generation algorithm is guaranteed to generate trajectories that satisfy all the constraints for the fine and coarse stages. We also describe a constrained model predictive control algorithm used to control the coarse stage. The simulation results of applying the developed methodology to track a pre-determined pattern is presented. ASME Proceedings -Advances in Motion ControlThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACTDual-stage positioning systems have been widely used in factory automation, robotic manipulators, high-density data storage systems, and manufacturing systems. Trajectory generation and control of dual-stage positioning systems is of great importance and is made complicated by the presence of physical and operational constraints. In this work, we describe how to generate feasible reference trajectories for a dual-stage positioning system consisting of a fine stage and a coarse stage, and how to use them in a model predictive control algorithm for which recursive feasibility is guaranteed. The reference generation algorithm is guaranteed to generate trajectories that satisfy all the constraints for the fine and coarse stages. We also describe a constrained model predictive control algorithm used to control the coarse stage. The results of applying the developed methodology to track a pre-determined pattern is presented.

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GPUs (Graphics Processing Units), traditionally used for 3D graphics calculations, have recently got an ability to perform general purpose calculations with a GPGPU (General Purpose GPU) technology. Moreover, GPUs can be much faster than CPUs (Central Processing Units) by performing hundreds or even thousands commands concurrently. This parallel processing allows the GPU achieving the extremely high performance but also requires using only highly parallel algorithms which can provide enough commands on each clock cycle. This work formulates a methodology for selection of a right geometry representation and a data structure suitable for parallel processing on GPU. Then the methodology is used for designing the 3-axis CNC milling simulation algorithm accelerated with the GPGPU technology. The developed algorithm is validated by performing an experimental machining simulation and evaluation of the performance results. The experimental simulation shows an importance of an optimization process and usage of algorithms that provide enough work to GPU. The used test configuration also demonstrates almost an order of magnitude difference between CPU and GPU performance results.